CN115453254B - Power quality monitoring method and system based on special transformer acquisition terminal - Google Patents
Power quality monitoring method and system based on special transformer acquisition terminal Download PDFInfo
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Abstract
The invention provides a power quality monitoring method and a system based on a private transformer acquisition terminal, which relate to the technical field of data processing, and are used for determining a data acquisition source of the private transformer acquisition terminal according to monitoring indexes of power quality evaluation and correspondingly constructing a data transmission channel, carrying out data characteristic recognition on the data acquisition source, outputting an abnormal recognition result, carrying out data correction on the abnormal recognition result to obtain a data conversion result, and transmitting the data conversion result to a power quality monitoring system for power quality evaluation. The technical problem that in the process of carrying out power quality evaluation on a specially-changed user in the prior art, the acquisition accuracy of power quality evaluation data is insufficient, so that the reference value of a power quality evaluation result in power operation and maintenance is low is solved. The method and the device have the advantages that the acquisition precision of relevant data of the power quality evaluation of the special transformer users is improved, the referenceability and the effectiveness of the power quality evaluation result of the special transformer users in power operation and maintenance are improved, and the technical effect of ensuring the power utilization safety of the special transformer users is achieved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a power quality monitoring method and system based on a private transformer acquisition terminal.
Background
The electric energy quality reflects whether the power grid and the power system are in a safe operation state, and good electric energy quality is an important guarantee for normal operation of economic production and national life quality, and is beneficial to guaranteeing normal operation of various electric equipment and avoiding reversible or irreversible damage of the electric equipment due to accurate real-time grasp of the electric energy quality condition.
With the continuous development of electric science and power grid systems in China, the importance of China on electric energy quality monitoring is increased continuously, but the center of electric energy quality assessment at the present stage is scientificity of electric energy quality assessment reference factors, and the data accuracy of the obtained electric energy quality assessment reference factors is not concerned.
In the prior art, in the process of carrying out power quality evaluation on a specially-changed user, the technical problem that the reference value of a power quality evaluation result in power operation and maintenance is low due to insufficient power quality evaluation data acquisition precision exists.
Disclosure of Invention
The application provides a power quality monitoring method and system based on a private transformer acquisition terminal, which are used for solving the technical problem that in the power quality evaluation process of a private transformer user in the prior art, the acquisition precision of power quality evaluation data is insufficient, so that the reference value of a power quality evaluation result in power operation and maintenance is low.
In view of the above problems, the present application provides a power quality monitoring method and system based on a private transformer acquisition terminal.
In a first aspect of the present application, there is provided a power quality monitoring method based on a private transformer acquisition terminal, the method comprising: connecting the power quality monitoring system to obtain a monitoring index set for power quality evaluation; determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set; determining a plurality of data transmission channels by taking the data acquisition source as a connection object; acquiring an abnormal feature set by carrying out data feature identification on the data acquisition source, wherein the abnormal feature set is a data set with abnormality in real-time acquisition data; model training is carried out by taking the abnormal feature set as a training set, and a plurality of abnormal recognition models are obtained, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels; outputting an abnormality recognition result according to the abnormality recognition models; inputting the abnormal identification result into the data conversion module, and obtaining a data conversion result according to the data conversion module; and transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels for power quality assessment.
In a second aspect of the present application, there is provided a power quality monitoring system based on a private transformer acquisition terminal, the system comprising: the detection index obtaining module is used for connecting with the power quality monitoring system to obtain a monitoring index set for carrying out power quality evaluation; the data source determining module is used for determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set; the data channel determining module is used for determining a plurality of data transmission channels by taking the data acquisition source as a connection object; the data characteristic identification module is used for acquiring an abnormal characteristic set by carrying out data characteristic identification on the data acquisition source, wherein the abnormal characteristic set is a data set with abnormality in real-time acquisition data; the recognition model training module is used for carrying out model training by taking the abnormal characteristic set as a training set to obtain a plurality of abnormal recognition models, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels; the abnormality identification output module is used for outputting an abnormality identification result according to the plurality of abnormality identification models; the data conversion execution module is used for inputting the abnormal identification result into the data conversion module and obtaining a data conversion result according to the data conversion module; and the power quality evaluation module is used for transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels and is used for performing power quality evaluation.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method provided by the embodiment of the application, the monitoring index set for carrying out electric energy quality assessment is obtained by connecting the electric energy quality monitoring system; determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set; providing a reference basis for the subsequent construction of the data transmission channels, and determining a plurality of data transmission channels by taking the data acquisition source as a connecting object; acquiring an abnormal feature set by carrying out data feature identification on the data acquisition source, wherein the abnormal feature set is a data set with abnormality in real-time acquisition data; the abnormal feature set is used as a training set for model training, a plurality of abnormal recognition models are obtained, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels, so that abnormal recognition is conveniently carried out on multi-type data by taking the data channels as units, the recognition accuracy and recognition efficiency of the abnormal data are improved, and an abnormal recognition result is output according to the plurality of abnormal recognition models; inputting the abnormal identification result into the data conversion module, obtaining a data conversion result according to the data conversion module, correcting abnormal data into normal data, and improving the validity of reference data for carrying out electric energy quality assessment in an electric energy quality monitoring model finally; and transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels for power quality assessment. The method and the device have the advantages that the acquisition precision of relevant data of the power quality evaluation of the special transformer users is improved, the referenceability and the effectiveness of the power quality evaluation result of the special transformer users in power operation and maintenance are improved, and the technical effect of ensuring the power utilization safety of the special transformer users is achieved.
Drawings
Fig. 1 is a schematic flow chart of a power quality monitoring method based on a private transformer acquisition terminal provided by the application;
fig. 2 is a schematic flow chart of a flag reminding abnormal acquisition device in a power quality monitoring method based on a private transformer acquisition terminal;
fig. 3 is a schematic flow chart of obtaining a data conversion result in the power quality monitoring method based on the private transformer acquisition terminal;
fig. 4 is a schematic structural diagram of an electrical energy quality monitoring system based on a private transformer acquisition terminal.
Reference numerals illustrate: the system comprises a detection index obtaining module 11, a data source determining module 12, a data channel determining module 13, a data characteristic identifying module 14, an identifying model training module 15, an abnormality identifying output module 16, a data conversion executing module 17 and a power quality evaluating module 18.
Detailed Description
The application provides a power quality monitoring method and system based on a private transformer acquisition terminal, which are used for solving the technical problem that in the power quality evaluation process of a private transformer user in the prior art, the acquisition precision of power quality evaluation data is insufficient, so that the reference value of a power quality evaluation result in power operation and maintenance is low. The method and the device have the advantages that the acquisition precision of relevant data of the power quality evaluation of the special transformer users is improved, the referenceability and the effectiveness of the power quality evaluation result of the special transformer users in power operation and maintenance are improved, and the technical effect of ensuring the power utilization safety of the special transformer users is achieved.
According to the technical scheme, the data are acquired, stored, used and processed according with relevant regulations of laws and regulations.
In the following, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention, and that the present invention is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
As shown in fig. 1, the present application provides a power quality monitoring method based on a private transformer acquisition terminal, where the method is applied to a power quality monitoring system, and the system is communicatively connected with a data conversion module, and the method includes:
s100, connecting the power quality monitoring system to obtain a monitoring index set for power quality evaluation;
specifically, it should be understood that the electric energy quality is a dynamic evaluation result of the electric energy quality in the reactive power system, which is obtained by comprehensively evaluating various data indexes such as voltage deviation, frequency deviation, harmonic wave and simplified harmonic wave. The monitoring of the electric energy quality is helpful for analyzing and determining whether the current electric energy quality has problems or not, so that a control strategy is adopted in time to eliminate or inhibit the electric energy dangerous problems, and the safe operation of electric equipment of various types is ensured.
In this embodiment, the power quality is dynamically monitored in real time based on the power quality monitoring system, and the power quality monitoring system outputs a power quality evaluation result by acquiring a plurality of power data monitoring indexes of the power utilization terminal. In order to realize accurate monitoring and evaluation of the power quality condition of the private transformer user, the power quality monitoring system based on the private transformer acquisition terminal is in communication connection with the power quality monitoring system, and the power quality monitoring system based on the private transformer acquisition terminal determines monitoring data for evaluating the power quality according to the power quality monitoring system to obtain the monitoring index set for evaluating the power quality of the private transformer user in the power quality monitoring system based on the private transformer acquisition terminal.
S200, determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set;
specifically, the private transformer users are users using the proprietary transformer to supply power, including private transformer users of large industry private transformer users, non-general industry private transformer users, commercial hybrid power users and the like, and because the private transformer users commonly have lack of full-time electricians compared with the public transformer users and have knowledge blind areas in the aspect of power business, the private transformer users cannot timely know whether dangerous states such as overload operation exist in the power utilization process, and therefore the influence of fluctuation caused by power fluctuation of electric equipment is caused. The special transformer acquisition terminal is used for acquiring various data for carrying out electric energy quality assessment in real time in various electric power data of special transformer users. The data acquisition source is a data acquisition device for monitoring dynamic power data of the power system.
In this embodiment, multiple types of monitoring data for evaluating the power quality of the private transformer user are determined according to the data items adopted by the monitoring index set for evaluating the power quality, and multiple data acquisition sources for performing data acquisition on the private transformer user by the private transformer acquisition terminal are determined according to the multiple types of monitoring data.
S300, determining a plurality of data transmission channels by taking the data acquisition source as a connection object;
s400, acquiring an abnormal feature set by carrying out data feature recognition on the data acquisition source, wherein the abnormal feature set is a data set with abnormality in real-time acquired data;
specifically, in this embodiment, the data transmission channel is configured to transmit information of data obtained by the data acquisition device at the data acquisition source, and send the data to the power quality monitoring system for power quality assessment.
The data transmission channels and the data acquisition sources have a corresponding relation, and each data transmission channel is used for transmitting data of the same data acquisition source singly.
Acquiring defect data of a plurality of data acquisition sources in historical data acquisition, acquiring a plurality of data defect characteristics corresponding to the plurality of data acquisition sources based on the historical defect data of the data acquisition sources, performing data characteristic identification based on real-time output data of the plurality of data acquisition sources corresponding to traversal of the plurality of data defect characteristics, and acquiring an abnormal characteristic set with abnormality in the real-time acquisition data, wherein the abnormal characteristic set reflects the data defect currently existing in the real-time acquisition data acquired based on the data acquisition sources, and the abnormal characteristic set is composed of abnormal data of the plurality of data acquisition sources.
S500, performing model training by taking the abnormal feature set as a training set to obtain a plurality of abnormal recognition models, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels;
further, the method step S500 provided in the present application further includes:
s510, constructing a transmission quality assessment model, wherein the transmission quality assessment model is connected with the plurality of abnormal recognition models;
s520, linking the transmission quality evaluation model to the plurality of data transmission channels, and performing transmission quality evaluation on the plurality of data transmission channels to obtain a plurality of transmission quality coefficients;
and S530, taking the transmission quality coefficients as constraint conditions for activating the anomaly identification models.
Specifically, in this embodiment, a plurality of abnormality recognition models are correspondingly set for a plurality of data acquisition sources, and in this embodiment, the method for constructing and training a data abnormality recognition model is not limited, and defect data existing in historical data acquisition of a plurality of data acquisition sources and the abnormal feature data set of a plurality of data acquisition sources are used as training data to perform model training, so as to obtain a plurality of abnormality recognition models with accuracy of model output results reaching a preset output accuracy requirement. And embedding the plurality of abnormality recognition models into the plurality of data transmission channels with the corresponding relation with the plurality of data acquisition sources according to the corresponding relation between the plurality of abnormality recognition models and the plurality of data acquisition sources.
Meanwhile, in order to avoid that data acquired by the data acquisition equipment are required to be identified and processed in the channel through the abnormal identification model, the abnormal identification model embedded into the data transmission channel is in an operating state in real time in the data transmission process of the data acquisition source, so that the system is wasted in computational power resources and the data transmission efficiency is reduced.
In this embodiment, an activation condition is set for the data anomaly identification model, and a plurality of anomaly identification models of a plurality of data transmission channels are determined according to the obtained data transmission quality coefficients, wherein the data transmission quality coefficients are obtained by performing data transmission quality evaluation on the real-time collected data in two dimensions of data transmission delay and data loss.
Specifically, in this embodiment, the data transmission quality evaluation is performed on the real-time collected data in terms of both data transmission delay and data transmission loss by building the transmission quality evaluation model.
Connecting the transmission quality evaluation model with the plurality of abnormal recognition models, linking the transmission quality evaluation model to the plurality of data transmission channels, carrying out transmission quality evaluation on real-time acquisition data transmitted in the plurality of data transmission channels to obtain a plurality of transmission quality coefficients, using the plurality of transmission quality coefficients as constraint conditions for activating the plurality of abnormal recognition models, comparing the transmission quality coefficients with activation coefficients preset by the plurality of abnormal recognition models, determining the corresponding abnormal recognition models which need to be activated, and carrying out abnormal recognition on the real-time acquisition data.
According to the embodiment, the plurality of abnormal recognition models are correspondingly embedded in the plurality of data transmission channels, and the transmission quality evaluation model for evaluating the data transmission quality is linked in the data transmission channels to perform activation judgment on the plurality of abnormal recognition models, so that the technical effects of accurately recognizing abnormal data in real-time acquired data and avoiding influence of abnormal data recognition on data transmission efficiency are achieved.
S600, outputting an abnormality recognition result according to the abnormality recognition models;
specifically, in this embodiment, the anomaly recognition result is a data deviation generated in the data transmission process of the data transmission channels, and according to a plurality of anomaly recognition models embedded in the plurality of data transmission channels, anomaly recognition of data in each data transmission channel is correspondingly performed, the anomaly recognition result is obtained, and data recovery and correction are performed on the anomaly recognition result, so that each item of related data, which is scheduled to be transmitted to the power quality detection system in each data transmission channel for power quality assessment, has credibility.
In this embodiment, the plurality of anomaly recognition models are respectively embedded in the plurality of data transmission channels, and the anomaly recognition models are correspondingly activated if and only if the transmission quality coefficient department obtained by data transmission channel data evaluation performed by the data quality evaluation model does not satisfy the activation coefficient preset by the anomaly recognition model. The preferred construction method of the anomaly identification model is to acquire data deviation generated in the data transmission process of a plurality of sample data transmission channel histories with the same data transmission function as the data transmission channel, and obtain a plurality of sample anomaly data and a plurality of groups of sample anomaly identification results mapped with the plurality of sample anomaly data.
The method comprises the steps of carrying out data identification and division on a plurality of sample abnormal data and a plurality of sample abnormal recognition results, respectively identifying and dividing part of data in the plurality of sample abnormal data and part of sample abnormal recognition results mapped with the part of data in the plurality of sample abnormal data into model training data, respectively identifying and dividing part of data in the plurality of sample abnormal data and part of sample abnormal recognition results mapped with the part of data in the plurality of sample abnormal data into model test data, and identifying and dividing the rest data into verification data.
And carrying out model construction based on a feedforward neural network, and carrying out iterative supervision training, verification and test on the abnormal recognition model through training data, test data and verification data to obtain the abnormal recognition model with the abnormal data recognition accuracy meeting the preset requirements.
And constructing and training multiple abnormal recognition models of a plurality of data transmission channels by adopting the same method, and outputting an abnormal recognition result according to the multiple abnormal recognition models.
S700, inputting the abnormal recognition result into the data conversion module, and obtaining a data conversion result according to the data conversion module;
further, as shown in fig. 3, the anomaly identification result is input into the data conversion module, and according to the data conversion module, a data conversion result is obtained, and the method step S700 provided in the present application further includes:
s710, obtaining an identification abnormal source through carrying out abnormal source analysis on the abnormal identification result, wherein the identification abnormal source is a data source with the largest abnormal data proportion;
s720, carrying out model training by using the identification abnormal source to generate a data conversion model, wherein the data conversion model is used for realizing calibration correction of abnormal data;
and S730, correcting the abnormal recognition result by the data conversion module embedded in the data conversion model, and outputting the data conversion result.
Specifically, in this embodiment, the data conversion module is configured to restore and correct the abnormal recognition result to normal data, and the identified abnormal source is a data source with the largest proportion in the abnormal data.
And determining the reasons for causing the data abnormality by carrying out abnormality source analysis on the abnormality identification result, wherein some equipment adopts remote data transmission or signal transmission, and the reasons for the data abnormality such as signal interference data transmission exist. And acquiring a data source with the largest proportion in the abnormal data as the identification abnormal source, wherein the identification abnormal source is representative of the abnormal condition of the data, and performing model training by using the identification abnormal source to generate a data conversion model for performing calibration correction of the abnormal data.
The method for constructing the data conversion model and the training method are not limited, and can be set according to actual requirements. Embedding the trained data conversion model into the data conversion module of the data conversion model, correcting the abnormal recognition result, and outputting the data conversion result.
According to the embodiment, through carrying out anomaly source analysis on various types of anomaly data, acquiring a data source with universality of occurrence of data anomalies from the anomaly source, and carrying out construction and training of an anomaly data correction model, the technical effects of carrying out directional targeted correction on the anomaly data, improving the transmission quality of a data transmission channel and indirectly improving the effectiveness and the credibility of an electric energy quality assessment result are achieved.
And S800, transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels for power quality assessment.
Specifically, in this embodiment, the data conversion result and the real-time collected data without data abnormality are transmitted to the power quality monitoring system through the plurality of data transmission channels, and the power quality monitoring system performs power quality assessment, and outputs a power quality assessment result.
The method provided by the embodiment obtains a monitoring index set for carrying out electric energy quality assessment by connecting the electric energy quality monitoring system; determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set; providing a reference basis for the subsequent construction of the data transmission channels, and determining a plurality of data transmission channels by taking the data acquisition source as a connecting object; acquiring an abnormal feature set by carrying out data feature identification on the data acquisition source, wherein the abnormal feature set is a data set with abnormality in real-time acquisition data; the abnormal feature set is used as a training set for model training, a plurality of abnormal recognition models are obtained, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels, so that abnormal recognition is conveniently carried out on multi-type data by taking the data channels as units, the recognition accuracy and recognition efficiency of the abnormal data are improved, and an abnormal recognition result is output according to the plurality of abnormal recognition models; inputting the abnormal identification result into the data conversion module, obtaining a data conversion result according to the data conversion module, correcting abnormal data into normal data, and improving the validity of reference data for carrying out electric energy quality assessment in an electric energy quality monitoring model finally; and transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels for power quality assessment. The method and the device have the advantages that the acquisition precision of relevant data of the power quality evaluation of the special transformer users is improved, the referenceability and the effectiveness of the power quality evaluation result of the special transformer users in power operation and maintenance are improved, and the technical effect of ensuring the power utilization safety of the special transformer users is achieved.
Further, as shown in fig. 2, the method steps provided in the present application further include:
s210, acquiring information of data acquisition equipment of the data acquisition source;
s220, extracting equipment working condition data from the information of the data acquisition equipment to acquire equipment real-time working condition information;
s230, judging whether the data acquisition equipment is in an abnormal state or not according to the real-time working condition information of the equipment;
s240, if the data acquisition equipment is in an abnormal state, carrying out identification reminding on the abnormal acquisition equipment.
Further, the method provided by the present application further includes:
s231, judging whether the data acquisition equipment is complete set acquisition equipment, wherein the complete set acquisition equipment is auxiliary equipment and auxiliary equipment for executing data acquisition;
s232, the data acquisition equipment is a complete set of acquisition equipment, and a data transmission path of the complete set of acquisition equipment is acquired;
s233, determining a data transmission abnormal source according to the data transmission path;
s234, positioning the abnormal acquisition equipment according to the data transmission abnormal source.
Specifically, in order to ensure the validity of the monitoring data obtained based on the data acquisition source for performing power quality evaluation of the specially-changed user, abnormality of the monitoring data due to abnormality of the sensing device performing data acquisition, namely, the data acquisition device is avoided. In the embodiment, before the data acquisition device performs private-change user data acquisition on the data acquisition source, whether the current data acquisition device has abnormal state is verified.
In this embodiment, information of a data acquisition device of the data acquisition source is acquired, device working condition data of the data acquisition device is acquired according to the information of the data acquisition device, the device working condition data and the device working condition data are actually acquired as the acquired device real-time working condition information, a working condition data fluctuation curve image is generated according to the device real-time working condition information, and whether the data acquisition device is in an abnormal state is judged according to whether the data fluctuation curve change is regular or not.
If the data acquisition equipment is in an abnormal state, the equipment composition type of the data acquisition equipment is further judged, the equipment composition type of the data acquisition equipment comprises single data acquisition equipment and complete set of acquisition equipment, the single data acquisition equipment is equipment for carrying out data source data acquisition based on single equipment, the complete set of data acquisition equipment is combined equipment for carrying out data acquisition by matching sub-equipment and mother equipment for assisting in executing data acquisition, a data transmission path in the complete set of data acquisition equipment is transmitted from the sub-equipment to the mother equipment, and the mother equipment and the sub-equipment are in one-to-one correspondence or one-to-many correspondence.
When the data acquisition equipment is single acquisition equipment, equipment abnormality identification reminding is carried out on the data acquisition equipment, and data acquisition operation of the data acquisition equipment is stopped.
When the data acquisition equipment is a complete set of acquisition equipment, the auxiliary execution data acquisition sub-equipment and the auxiliary execution data acquisition parent equipment in the complete set of acquisition equipment are further determined, the data transmission path is obtained by acquiring the auxiliary execution data acquisition sub-equipment and the auxiliary execution data acquisition parent equipment in the complete set of acquisition equipment, the data transmission source is determined in the data transmission process or at the data transmission source according to the data transmission path, the abnormal acquisition equipment is positioned according to the data transmission abnormal source, the abnormal acquisition parent equipment is positioned when the data transmission abnormal source is in the data transmission process, the abnormal acquisition sub-equipment is correspondingly positioned when the data transmission abnormal source is at the data transmission source, the abnormal acquisition equipment is identified and reminded, and the data screen used for carrying out data acquisition when the data source data acquisition is carried out later is divided to avoid that error data flows into the power quality monitoring system to cause the reduction of the referential of the power quality assessment result.
According to the embodiment, before data acquisition of the data source is executed, operation condition analysis is carried out on equipment for carrying out data acquisition, the data acquisition equipment with abnormal conditions is identified, abnormal data is prevented from flowing into the power quality detection system to participate in power quality assessment, and the technical effects of improving the validity and referenceability of the power quality assessment result of the special transformer user are achieved.
Further, after determining a plurality of data transmission channels by using the data acquisition source as a connection object, the method step S520 provided in the present application further includes:
s521, determining a plurality of groups of test samples according to the plurality of data transmission channels, wherein the plurality of groups of test samples correspond to the plurality of data transmission channels, and each group of test samples comprises a plurality of groups of data sets;
s522, carrying out transmission frame period test on the plurality of data transmission channels based on the plurality of test samples, and outputting a plurality of transmission delay indexes, wherein the plurality of transmission delay indexes are in one-to-one correspondence with the plurality of data transmission channels;
and S523, calculating the transmission delay indexes as a first data set to generate the transmission quality coefficients.
Further, generating the plurality of transmission quality coefficients, the method provided in the present application further includes:
s523-1, acquiring a plurality of output samples obtained through the plurality of data transmission channels based on the plurality of test samples;
s523-2, carrying out data loss analysis by using the plurality of test samples and the plurality of output samples, and outputting a plurality of data loss indexes, wherein the plurality of data loss indexes are in one-to-one correspondence with the plurality of data transmission channels;
and S523-3, calculating the plurality of data loss indexes as a second data set to generate the plurality of transmission quality coefficients.
Specifically, the transmission frame period test is a transmission frame period generated by performing a transmission period test according to a response time from a time node when the transmission data is sent by the data acquisition device as a sending end to when the transmission data arrives at the power quality monitoring system as a receiving end through the data transmission channel to receive the transmission data.
In this embodiment, multiple sets of test samples are determined according to the multiple data transmission channels, where data types of the multiple sets of test samples correspond to transmission data types of the multiple data transmission channels, and each set of test samples includes multiple sets of data sets with different data sizes.
And carrying out transmission frame period test on the plurality of data transmission channels based on the plurality of test samples, and outputting a plurality of transmission delay indexes according to the data transmission frame periods of the plurality of test samples in the plurality of data transmission channels, wherein the transmission delay indexes are in units of time.
Based on the plurality of test samples, obtaining a plurality of output samples obtained through the plurality of data transmission channels, performing data loss analysis according to the plurality of test samples and the plurality of output samples, and outputting a plurality of data loss indexes according to the data loss degree between the test samples and the output samples, wherein the data loss indexes take data occupation space as a unit, and the plurality of data loss indexes are in one-to-one correspondence with the plurality of data transmission channels.
And normalizing the transmission delay indexes and the data loss indexes, taking the transmission delay indexes as a first data set, and taking the data loss indexes as a second data set to calculate so as to generate the transmission quality coefficients, wherein the transmission quality coefficients are in one-to-one correspondence with the data transmission channels.
According to the embodiment, the transmission quality evaluation model is built to be linked into a plurality of data transmission channels and used for evaluating and judging whether the abnormal recognition model embedded in each data transmission channel needs to be activated or not in two dimensions of data transmission delay and data transmission loss, so that the technical effects of reducing the consumption of the abnormal recognition model on system computing power resources and avoiding the reduction of data transmission efficiency caused by the fact that all data of the data transmission channels need to be subjected to abnormal data recognition analysis, and the reduction of the timeliness of the electric energy quality obtained by the electric energy quality monitoring model are achieved.
Example two
Based on the same inventive concept as the power quality monitoring method based on the private transformer acquisition terminal in the foregoing embodiment, as shown in fig. 4, the present application provides a power quality monitoring system based on the private transformer acquisition terminal, where the system includes:
the detection index obtaining module 11 is used for connecting with the power quality monitoring system to obtain a monitoring index set for carrying out power quality evaluation;
a data source determining module 12, configured to determine a data acquisition source for the private transformer acquisition terminal according to the monitoring index set;
a data channel determining module 13, configured to determine a plurality of data transmission channels by using the data acquisition source as a connection object;
the data feature recognition module 14 is configured to obtain an abnormal feature set by performing data feature recognition on the data acquisition source, where the abnormal feature set is a data set with an abnormality in real-time acquired data;
the recognition model training module 15 is configured to perform model training with the abnormal feature set as a training set, and obtain a plurality of abnormal recognition models, where the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels;
an anomaly identification output module 16 for outputting an anomaly identification result according to the plurality of anomaly identification models;
the data conversion execution module 17 is configured to input the anomaly identification result into a data conversion module, and obtain a data conversion result according to the data conversion module;
the power quality evaluation module 18 is configured to transmit the data conversion results from the plurality of data transmission channels to the power quality monitoring system for performing power quality evaluation.
Further, the data source determining module 12 further includes:
the device information obtaining unit is used for obtaining information of the data acquisition device of the data acquisition source;
the equipment working condition acquisition unit is used for acquiring real-time working condition information of the equipment by extracting equipment working condition data from the information of the data acquisition equipment;
the device state judging unit is used for judging whether the data acquisition device is in an abnormal state or not according to the real-time working condition information of the device;
and the equipment abnormality identification unit is used for carrying out identification reminding on the abnormal acquisition equipment if the data acquisition equipment is in an abnormal state.
Further, the device state judging unit further includes:
the equipment constitution judging unit is used for judging whether the data acquisition equipment is complete set acquisition equipment or not, wherein the complete set acquisition equipment is auxiliary equipment and auxiliary equipment for executing data acquisition;
the data transmission judging unit is used for acquiring a data transmission path of the whole set of acquisition equipment, wherein the data acquisition equipment is the whole set of acquisition equipment;
an abnormal source determining unit, configured to determine a data transmission abnormal source according to the data transmission path;
and the abnormal equipment positioning unit is used for positioning the abnormal acquisition equipment according to the data transmission abnormal source.
Further, the recognition model training module 15 further includes:
an evaluation model building unit for building a transmission quality evaluation model, wherein the transmission quality evaluation model is connected with the plurality of anomaly identification models;
the evaluation model link unit is used for linking the transmission quality evaluation model to the plurality of data transmission channels, and carrying out transmission quality evaluation on the plurality of data transmission channels to obtain a plurality of transmission quality coefficients;
and the constraint condition presetting unit is used for taking the transmission quality coefficients as constraint conditions for activating the anomaly identification models.
Further, the data channel determining module 13 further includes:
the test sample determining unit is used for determining a plurality of groups of test samples according to the plurality of data transmission channels, wherein the plurality of groups of test samples correspond to the plurality of data transmission channels, and each group of test samples comprises a plurality of groups of data sets;
a transmission period testing unit, configured to perform a transmission frame period test on the plurality of data transmission channels based on the plurality of test samples, and output a plurality of transmission delay indexes, where the plurality of transmission delay indexes are in one-to-one correspondence with the plurality of data transmission channels;
and the transmission quality calculation unit is used for calculating the plurality of transmission delay indexes as a first data set and generating the plurality of transmission quality coefficients.
Further, the transmission quality calculation unit further includes:
an output sample obtaining unit, configured to obtain a plurality of output samples obtained through the plurality of data transmission channels based on the plurality of test samples;
the data loss analysis unit is used for carrying out data loss analysis on the plurality of test samples and the plurality of output samples and outputting a plurality of data loss indexes, wherein the plurality of data loss indexes are in one-to-one correspondence with the plurality of data transmission channels;
and the transmission quality generation unit is used for calculating the plurality of data loss indexes as a second data set to generate the plurality of transmission quality coefficients.
Further, the data conversion executing module 17 further includes:
the abnormal source analysis unit is used for obtaining an identification abnormal source through carrying out abnormal source analysis on the abnormal identification result, wherein the identification abnormal source is a data source with the largest abnormal data proportion;
the abnormal data correction unit is used for carrying out model training by using the identification abnormal source to generate a data conversion model, wherein the data conversion model is used for realizing calibration correction of abnormal data;
and the data conversion output unit is used for correcting the abnormal recognition result by the data conversion module embedded in the data conversion model and outputting the data conversion result.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memories, and identified by various non-limiting types of computer processors, thereby implementing any of the methods or steps described above.
Based on the above-mentioned embodiments of the present invention, any improvements and modifications to the present invention without departing from the principles of the present invention should fall within the scope of the present invention.
Claims (4)
1. The power quality monitoring method based on the private transformer acquisition terminal is characterized by being applied to a power quality monitoring system, wherein the system is in communication connection with a data conversion module, and the method comprises the following steps:
connecting the power quality monitoring system to obtain a monitoring index set for power quality evaluation;
determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set;
determining a plurality of data transmission channels by taking the data acquisition source as a connection object;
acquiring an abnormal feature set by carrying out data feature identification on the data acquisition source, wherein the abnormal feature set is a data set with abnormality in real-time acquisition data;
model training is carried out by taking the abnormal feature set as a training set, and a plurality of abnormal recognition models are obtained, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels;
constructing a transmission quality evaluation model, wherein the transmission quality evaluation model is connected with the plurality of anomaly identification models;
the transmission quality evaluation model is linked to the plurality of data transmission channels, and the plurality of data transmission channels are subjected to transmission quality evaluation to obtain a plurality of transmission quality coefficients;
taking the plurality of transmission quality coefficients as constraint conditions for activating the plurality of anomaly identification models;
determining a plurality of groups of test samples according to the plurality of data transmission channels, wherein the plurality of groups of test samples correspond to the plurality of data transmission channels, and each group of test samples comprises a plurality of groups of data sets;
performing transmission frame period test on the plurality of data transmission channels based on the plurality of groups of test sample books, and outputting a plurality of transmission delay indexes, wherein the plurality of transmission delay indexes are in one-to-one correspondence with the plurality of data transmission channels;
calculating the transmission delay indexes as a first data set to generate the transmission quality coefficients;
based on the multiple groups of test samples, multiple groups of output samples obtained through the multiple data transmission channels are obtained;
carrying out data loss analysis by using the plurality of groups of test samples and the plurality of groups of output samples, and outputting a plurality of data loss indexes, wherein the plurality of data loss indexes are in one-to-one correspondence with the plurality of data transmission channels;
calculating the plurality of data loss indexes as a second data set to generate the plurality of transmission quality coefficients;
outputting an abnormality recognition result according to the abnormality recognition models;
inputting the abnormal identification result into the data conversion module, and obtaining a data conversion result according to the data conversion module;
obtaining an identification abnormal source through carrying out abnormal source analysis on the abnormal identification result, wherein the identification abnormal source is a data source with the largest abnormal data proportion;
training the model by using the identification abnormal source to generate a data conversion model, wherein the data conversion model is used for realizing calibration and correction of abnormal data;
correcting the abnormal recognition result by the data conversion module embedded in the data conversion model, and outputting the data conversion result;
and transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels for power quality assessment.
2. The method of claim 1, wherein the method further comprises:
acquiring information of data acquisition equipment of the data acquisition source;
extracting equipment working condition data from the information of the data acquisition equipment to acquire real-time working condition information of the equipment;
judging whether the data acquisition equipment is in an abnormal state or not according to the real-time working condition information of the equipment;
and if the data acquisition equipment is in an abnormal state, carrying out identification reminding on the abnormal acquisition equipment.
3. The method of claim 2, wherein the method further comprises:
judging whether the data acquisition equipment is complete set acquisition equipment or not, wherein the complete set acquisition equipment is auxiliary equipment and auxiliary equipment for executing data acquisition;
the data acquisition equipment is a complete set of acquisition equipment, and a data transmission path of the complete set of acquisition equipment is acquired;
determining a data transmission abnormal source according to the data transmission path;
and positioning the abnormal acquisition equipment according to the data transmission abnormal source.
4. A power quality monitoring system based on a private transformer acquisition terminal, the system being configured to perform a power quality monitoring method based on a private transformer acquisition terminal as set forth in claims 1 to 3, wherein the system includes:
the detection index obtaining module is used for connecting with the power quality monitoring system to obtain a monitoring index set for carrying out power quality evaluation;
the data source determining module is used for determining a data acquisition source for the special transformer acquisition terminal according to the monitoring index set;
the data channel determining module is used for determining a plurality of data transmission channels by taking the data acquisition source as a connection object;
the data characteristic identification module is used for acquiring an abnormal characteristic set by carrying out data characteristic identification on the data acquisition source, wherein the abnormal characteristic set is a data set with abnormality in real-time acquisition data;
the recognition model training module is used for carrying out model training by taking the abnormal characteristic set as a training set to obtain a plurality of abnormal recognition models, wherein the plurality of abnormal recognition models are respectively embedded in the plurality of data transmission channels;
the abnormality identification output module is used for outputting an abnormality identification result according to the plurality of abnormality identification models;
the data conversion execution module is used for inputting the abnormal identification result into the data conversion module and obtaining a data conversion result according to the data conversion module;
and the power quality evaluation module is used for transmitting the data conversion result to the power quality monitoring system through the plurality of data transmission channels and is used for performing power quality evaluation.
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| CN202211411814.5A CN115453254B (en) | 2022-11-11 | 2022-11-11 | Power quality monitoring method and system based on special transformer acquisition terminal |
| PCT/CN2023/121728 WO2024098990A1 (en) | 2022-11-11 | 2023-09-26 | Special transformer acquisition terminal-based electric energy quality monitoring method and system |
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| CN115453254B (en) * | 2022-11-11 | 2023-05-02 | 浙江万胜智能科技股份有限公司 | Power quality monitoring method and system based on special transformer acquisition terminal |
| CN117239747B (en) * | 2023-11-16 | 2024-02-06 | 江苏濠汉信息技术有限公司 | Dormitory safety electricity utilization control method and system based on model identification |
| CN117408537B (en) * | 2023-12-15 | 2024-05-07 | 安徽科派自动化技术有限公司 | Electric energy quality monitoring system capable of realizing real-time risk prediction |
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